The purpose of this project is to develop methodology for analyzing molecular population genetics data. Work has focused on the use of nonparametric methods for localizing susceptibility loci for complex diseases and quantitative traits in humans. A valid test of linkage and association was constructed that can use data from related nuclear families from large extended pedigrees. Computer simulations show that this new test can be substantially more powerful than existing methods that can use only a subset of the data. Furthermore, simulations suggest that the new test remains more powerful even when there is misclassification of unaffected individuals. Work has continued on studying the feasibility of using association tests to help fine map susceptability genes for complex diseases in outbred populations. Based upon a single marker analysis, we find that a simple strategy of choosing the marker with the smallest associated p-value to locate the disease locus performs reasonably well, especially if the susceptability allele is common. We also investigated a strategy of pooling adjacent markers to form haplotypes . Using multiple degrees of freedom chi-square tests for two, three and four pooled adjacent markers, we foundthat for a common disease allele there is no clear advantage of this form of pooling over a single marker analysis.